This chapter explores the innovative application of machine learning techniques to understand and predict the stability of protein substructures. Accurately identifying stable substructures within proteins necessitates incorporating the local context, crucial for elucidating the roles of supersecondary structures. This approach emphasizes the importance of contextual information in understanding the stability and functionality of protein regions, thereby providing a more comprehensive view of protein mechanics and interactions. The chapter focuses on our findings regarding the DnaK Hsp70 chaperone protein, utilizing it as a case study. This research highlights how context-dependent physico-chemical features derived from protein sequences can accurately classify residues into stable and unstable substructures by leveraging logistic regression, random forest, and support vector machine methods. The findings represent a pivotal step towards the rational design of proteins with tailored properties, offering new insights into protein engineering and the fundamental principles underpinning protein supersecondary structures.